CN112491067A - Active power distribution network capacity configuration method based on composite energy storage - Google Patents

Active power distribution network capacity configuration method based on composite energy storage Download PDF

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CN112491067A
CN112491067A CN202011305304.0A CN202011305304A CN112491067A CN 112491067 A CN112491067 A CN 112491067A CN 202011305304 A CN202011305304 A CN 202011305304A CN 112491067 A CN112491067 A CN 112491067A
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energy storage
composite energy
power
storage system
composite
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Inventor
姚艳
郭高鹏
康家乐
岑银伟
汪雅静
张志刚
张帅
江涵
宋弘亮
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Ningbo Electric Power Design Institute Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a capacity configuration method of an active power distribution network based on composite energy storage, belonging to the technical field of power grid planning, and comprising S1, establishing a composite energy storage system model applied to the active power distribution network; s2, analyzing the composite energy storage capacity configuration in the distributed power supply based on the composite energy storage system model to obtain a first capacity configuration scheme; s3, analyzing energy storage optimization configuration of each micro-grid group based on the composite energy storage system model to obtain a second capacity configuration scheme; and S4, performing multi-objective optimization on the first and second capacity allocation schemes according to the adaptive function and the adaptive weight, and realizing reasonable capacity allocation of the composite energy storage system. The optimal configuration objective function reduces distributed energy fluctuation and load power shortage rate, achieves reasonable capacity configuration of the composite energy storage system, and guarantees power supply reliability and economic operation of the system.

Description

Active power distribution network capacity configuration method based on composite energy storage
Technical Field
The invention relates to the technical field of power grid planning, in particular to a capacity configuration method of an active power distribution network based on composite energy storage.
Background
In recent years, under the dual pressure of energy exhaustion and environmental protection, a micro-grid generates electricity by utilizing new energy according to local conditions, has the characteristics of low cost, small pollution, flexible operation mode and the like, and has the advantages of natural disaster resistance and electric power safety guarantee, and becomes one of the hot spots concerned in the world electrical field.
The composite energy storage plays multiple roles in aspects of micro-grid power balance, smooth renewable energy fluctuation, improvement of electric energy quality and the like.
The composite energy storage is formed by polymerizing single energy storage with complementary advantages of different characteristics, has obvious advantages, can realize the complementary advantages of different energy storage, exerts respective characteristics and expands the space for exerting the advantages of different energy storage devices; the combination and complementation of power and energy characteristics can be realized, so that multiple requirements of a power grid are met, and the power supply reliability is improved; different energy storage devices are enabled to run in the optimized working interval of the energy storage devices through a regulation and control means, the charging and discharging states of the devices are optimized, and the service cycle and the cycle life are prolonged; under the rational configuration, reduce energy memory's operation cost, optimize utilization ratio, enlarge the industrial market, obtain great profit. In energy scheduling management and microgrid integrated control, energy storage capacity optimal configuration is a key problem, and the rationality of configuration directly influences the utilization rate of a distributed power supply and the economy and stability of a microgrid system. Meanwhile, problems exist, such as insufficient output power of an energy storage system or insufficient capacity of the system, which results in insufficient storage capacity of distributed energy generation, and load requirements cannot be well met. The capacity is enlarged as a solution to the problem, and the initial and operation and maintenance costs are increased. Therefore, the capacity allocation of the composite energy storage system is an urgent problem to be solved at present.
Disclosure of Invention
The invention aims to provide an active power distribution network capacity configuration method based on composite energy storage, which comprises the steps of establishing a composite energy storage system model, analyzing composite energy storage capacity configuration during distributed generation and energy storage optimal configuration in each micro-grid group based on the composite energy storage system model, establishing corresponding objective functions, combining adaptive functions and adaptive weights, carrying out multi-objective optimization to obtain optimal configuration objective functions, reducing distributed energy fluctuation and load power shortage, realizing reasonable capacity configuration of the composite energy storage system, and ensuring power supply reliability and economic operation of the system.
In a first aspect, the above object of the present invention is achieved by the following technical solutions:
a capacity configuration method of an active power distribution network based on composite energy storage comprises the following steps:
s1, establishing a composite energy storage system model applied to the active power distribution network;
s2, analyzing the composite energy storage capacity configuration in the distributed power supply based on the composite energy storage system model to obtain a first capacity configuration scheme;
s3, analyzing energy storage optimization configuration of each micro-grid group based on the composite energy storage system model to obtain a second capacity configuration scheme;
and S4, performing multi-objective optimization on the first and second capacity allocation schemes according to the adaptive function and the adaptive weight, and realizing reasonable capacity allocation of the composite energy storage system.
The invention is further configured to: in step S1, the composite energy storage system model includes at least one microgrid and a composite energy storage device, and the composite energy storage device includes a super storage battery and a super capacitor; the super storage battery is connected to the direct current bus through a first conversion power supply; the super capacitor is connected to the direct current bus through a second conversion power supply; the distributed energy is connected to the microgrid system through the converter; each micro-grid comprises a power type energy storage mode and an energy type energy storage mode, and each micro-grid and the composite energy storage device are connected in parallel.
The invention is further configured to: in step S2, the power of the composite energy storage system is analyzed, a target function and a constraint condition are established, and a second capacity allocation scheme is obtained.
The invention is further configured to: composite energy storage system power PH(t) is represented by the following formula:
PH(t)＝PLi(t)+PSC(t)＝PN(t)-Pout(t) (1)；
rated output power P of composite energy storage systemHESSThe following formula:
Figure BDA0002788143470000031
rated output energy E of composite energy storage systemHESSSatisfies the following formula:
Figure BDA0002788143470000032
in the formula, PLi(t) represents the output power of the lithium battery; pSC(t) represents the output power of the super capacitor; pN(t) represents the output power of the distributed power supply; pout(t) represents the output power supplied to the hybrid; eta1Represents the efficiency of the DC-DC conversion power supply; eta2Represents the efficiency of the DC-AC conversion power supply; etaLiRepresents the charging efficiency of the lithium battery; etaSCRepresenting the charging efficiency of the super capacitor; h represents the operation period duration; pN_min(t) represents a minimum value of distributed energy output at a certain time; pout_max(t) represents a maximum load demand at a time; pLi_0(t) represents the real-time power of the lithium battery; pSC_0(t) represents the real-time power of the supercapacitor; SOCLi_0Representing the initial state of charge of the lithium battery; SOCLi_maxRepresents the upper limit of the state of charge of the lithium battery; SOCLi_minRepresents a lower limit of the state of charge of the lithium battery; SOCSC_0Representing the initial state of charge of the super capacitor; SOCSC_maxRepresents the upper limit of the state of charge of the super capacitor; SOCSC_minRepresents the state of charge of the super capacitor
The invention is further configured to: establishing a first objective function f by taking the minimum absolute value of the adjusted power change difference value as an optimization target1Comprises the following steps:
Figure BDA0002788143470000041
the constraint conditions include: the storage battery works under the condition that the storage battery does not exceed a set interval; the output energy change meets the difference change between the distributed energy and the load; the requirement of maximum power fluctuation is met, and if the power supply of the distributed energy source is stopped suddenly, the energy storage device needs to respond and support quickly;
in the formula, PH_0(t) represents the real-time power of the composite stored energy output.
The invention is further configured to: in step S3, after the microgrid bus is connected to the energy storage system, a second objective function f is established with the minimum power shortage of the system as an optimization objective2And a constraint, wherein the second objective function f2Comprises the following steps:
Figure BDA0002788143470000042
in the formula, Pq(t) power output lacking in the system; pL(t) represents the total load power on the bus; t is a calculation time period.
The invention is further configured to: the constraint conditions include: meet the configuration rated output power P of the energy storage systemHESS(ii) a Energy storage system configuration rated output energy EHESS(ii) a The storage battery works under the condition of not crossing the wire; the output energy change meets the difference change between the distributed energy and the load; meet the maximum powerThe fluctuation requirement is that if the distributed energy source suddenly stops supplying power, the energy storage device needs to quickly respond to support; under the condition of dynamic balance among each micro-grid group, the output power P of the composite energy storage systemH_1(t) satisfies:
Figure BDA0002788143470000043
in the formula, PH_1(t) representing the real-time output power of the microgrid; n is the number of nodes of the micro-network; pL_j(t)
Representing the load power on the bus; m represents the number of loads, PG(t) represents the net real time power.
The invention is further configured to: in step S4, a first objective function f in the first capacity allocation plan is configured1Second objective function f in a second capacity allocation scheme2Performing multi-objective optimization to obtain an objective function f as:
f＝min(c1f1+c2f2) (18)；
combining constraint conditions, solving the multiple targets by adopting an improved genetic algorithm, and normalizing to obtain an optimized target function f:
Figure BDA0002788143470000051
wherein r represents the number of objective functions and z represents the number of chromosomes; f. ofr,z,maxThe maximum value output by the objective function r when the optimization is carried out by adopting an improved genetic algorithm is shown; f. ofr,z,minRepresents the minimum value output by the objective function r when the optimization is carried out by adopting an improved genetic algorithm.
In a second aspect, the above object of the present invention is achieved by the following technical solutions:
a capacity configuration terminal of an active power distribution network based on composite energy storage comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the capacity configuration method is realized when the processor executes the computer program.
In a third aspect, the above object of the present invention is achieved by the following technical solutions:
a computer-readable storage medium, storing a computer program which, when executed by the processor, implements the capacity configuration method of the present application.
Compared with the prior art, the beneficial technical effects of this application do:
1. according to the method, a composite energy storage system model is established, a target function is established based on the model, the target function is subjected to multi-objective optimization, an optimization scheme is obtained, and the capacity optimization configuration guidance of the composite energy storage system is realized theoretically;
2. furthermore, capacity configuration during distributed power supply and microgrid is considered, so that comprehensive analysis of the distributed power supply and the microgrid is realized;
3. furthermore, the weight of the distributed power supply and the weight of the micro-grid are considered, the contribution of the distributed power supply and the micro-grid to the capacity is comprehensively considered, the influence of distributed energy fluctuation and load power shortage on the system is reduced, and the power supply reliability is improved.
Drawings
FIG. 1 is a schematic diagram of a composite energy storage system model architecture of an embodiment of the present application;
fig. 2 is a schematic structural diagram of a composite energy storage system according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Detailed description of the preferred embodiment
The application provides an active power distribution network capacity configuration method based on composite energy storage, which comprises the following steps:
firstly, establishing a composite energy storage system model applied to an active power distribution network; as shown in fig. 1, the energy storage system comprises a composite energy storage device, a micro grid 1 to a micro grid n and a load, wherein the composite energy storage device, the micro grid 1 to the micro grid n and the load are connected in parallel. The BRK0/BRK11/BRK12/BRKS are circuit breakers and are used for connecting or disconnecting the composite energy storage system model with a power grid system.
In this embodiment, the power grid is 10KV, and the microgrid is 400V.
Each micro-grid comprises two energy storage modes, namely a power type energy storage mode and an energy type energy storage mode, and the two energy storage modes are used for coordinating each distributed energy source. The micro-grids are connected in parallel and used for coordinating the optimized operation among the micro-grids, and the economical and effective operation of the system is ensured under the condition of reducing power fluctuation.
The structure of the composite energy storage device is shown in fig. 2, and comprises a first power supply which is connected to a bus through a first AC converter DC/AC 1; the second power supply is connected to the bus through an AC/DC conversion device DC/AC 2; and the lithium battery is connected with a third AC device DC/AC3 through DC/DC1 conversion, the super capacitor is also connected with a third AC device DC/AC3 through DC/DC2 conversion, and the third AC device DC/AC3 is connected to the bus.
The DC/DC1 and the DC/DC2 are used for enabling direct currents on the lithium battery and the super capacitor to be subjected to DC/DC conversion respectively to obtain stable voltages, and the stable voltages are connected to a bus through an alternating current device.
And analyzing the composite energy storage capacity configuration under different conditions based on the composite energy storage system model.
Secondly, considering the configuration of the composite energy storage capacity under the condition of the distributed power supply, and respectively considering the power P of the composite energy storage systemH(t) rated output power PHESSReal time power PH_0And the state of charge SOC of the lithium batteryLi_KSOC of super capacitorSC_KAnd the parameters are equal, and a first objective function and a constraint condition under the condition are obtained.
In this case, the composite energy storage system power PH(t) is represented by the following formula:
PH(t)＝PLi(t)+PSC(t)＝PN(t)-Pout(t) (1)；
in the formula, PLi(t) represents the output power of the lithium iron phosphate battery; pSC(t) represents the output power of the super capacitor; pN(t) represents the output power of the distributed power supply; pout(t) represents the output power supplied to the hybrid.
One operation period h, in this embodiment, h is 24 hours, and the energy storage system is configured with the rated output power P in consideration of the converter efficiency, the charging efficiency of the energy storage system, and the maximum load demandHESSThe following formula:
Figure BDA0002788143470000081
in the formula eta1Represents the efficiency of the DC-DC conversion power supply; eta2Represents the efficiency of the DC-AC conversion power supply; etaLiRepresenting the charging efficiency of the lithium iron phosphate battery; etaSCRepresenting the charging efficiency of the super capacitor; h represents the operation period duration; pN_min(t) represents a minimum value of distributed energy output at a certain time; pout_max(t) represents a maximum load demand at a certain time.
In this embodiment, when h is 24 hours, formula 2 is as follows:
Figure BDA0002788143470000082
real-time power P of energy storage systemH_0Comprises the following steps:
Figure BDA0002788143470000083
SOC of lithium iron phosphate batteryLi_KAs shown in the following formula:
Figure BDA0002788143470000084
in the formula, PLi_0Representing the real-time power, SOC, of a lithium iron phosphate batteryLi_0Represents the initial state of charge of the lithium iron phosphate battery, ELiRepresenting the capacity of the lithium iron phosphate battery.
Super capacitor state of charge SOCSC_KAs shown in the following formula:
Figure BDA0002788143470000091
in the formula, PSC_0Representing real-time power, SOC, of a super-capacitorSC_0Indicating the initial state of charge of the supercapacitor, ESCRepresenting the capacity of the supercapacitor.
Accordingly, the overall state of charge SOC of the hybrid energy storage systemKAs shown in the following formula:
Figure BDA0002788143470000092
in the formula, PH_0Representing real-time power, SOC, of a composite energy storage system0Representing the initial state of charge of the composite energy storage system, EHRepresenting the capacity of the composite energy storage system.
In an operation period, the efficiency of the converter and the charging efficiency of the energy storage system are combined, the maximum requirement of the load is met, and the energy storage system is configured with rated output energy EHESSSatisfies the following conditions:
Figure BDA0002788143470000093
in this embodiment, if one operation period is 24 hours, the formula (7) is changed to
Figure BDA0002788143470000094
Figure BDA0002788143470000101
In the formula, PLi_0(t) represents the real-time power of the lithium battery; pSC_0(t) represents the real-time power of the supercapacitor; SOCLi_0Representing the initial state of charge of the lithium battery; SOCLi_maxRepresents the upper limit of the state of charge of the lithium battery; SOCLi_minLower limit for indicating state of charge of lithium battery；SOCSC_0Representing the initial state of charge of the super capacitor; SOCSC_maxRepresents the upper limit of the state of charge of the super capacitor; SOCSC_minRepresenting the lower limit of the state of charge of the supercapacitor.
To smooth the power output of the distributed energy source, the target function f is established by taking the minimum absolute value of the adjusted power change difference as an optimization target1Comprises the following steps:
Figure BDA0002788143470000102
the constraints to be satisfied include:
1. in order to prolong the service life of the energy storage system, the work of the lithium battery and the super capacitor is required not to exceed a set interval, namely:
Figure BDA0002788143470000103
2. the output energy change of the energy storage system meets the difference change between the distributed energy and the load:
ΔELi(t)+ΔESC(t)＝|ΔEN(t)-ΔEout(t)|
(10)；
ΔELi(t) represents the energy change, Δ E, of a lithium batterySC(t) represents the energy change of the supercapacitor; delta EN(t) represents the energy variation of the distributed energy source, Δ Eout(t) represents a load energy change.
3. The composite energy storage needs to meet the maximum power fluctuation requirement, and if the power supply of the distributed energy source is stopped suddenly, the energy storage device needs to respond and support quickly:
PH_0(t)≥ΔPmax (11)；
ΔPmaxrepresenting the maximum power fluctuation requirement in the system;
when energy storage optimization configuration under the condition of considering the distributed power supply, the method can satisfy the formulas (9) and (10),
(11) In addition to the constraints of (2), the basic conditions of the formulae (1), (2), and (7) need to be satisfied.
And thirdly, considering the energy storage optimization configuration of each microgrid group.
In order to coordinate dynamic balance among all micro-grid groups under an active power distribution network and output power of a composite energy storage system, the following expression needs to be satisfied:
Figure BDA0002788143470000111
in the formula, Pmicro_l(t) represents the real-time output power of the microgrid, n represents the number of nodes of the microgrid, PL_j(t) represents the load power on the 400V bus, m represents the number of loads, PG(t) represents the net real time power.
When each microgrid transmits power to a power grid system, whether the overall output of a microgrid group meets the power supply requirement of a load on a 400V bus needs to be considered.
When the overall output of the micro-grid group can meet the power supply requirement of the load on the bus, whether the composite energy storage system on the bus can be fully charged or not is considered. If the composite energy storage system can be fully charged, the redundant generated energy is transmitted to the power grid system, and if the composite energy storage system is not fully charged, the energy stored in the composite energy storage system changes along with time, as shown in the following formula:
EH_l(t+1)＝ELi_l(t+1)+ESC_l(t+1)
＝ELi_l(t)ηLi+ESC_l(t)ηSC+[Pmicro_l(t)-PL-j(t)]t
(13)；
and when the integral output of the micro-grid group does not meet the power supply requirement of the load on the bus, electricity needs to be purchased to the power grid system.
PG(t)＝PL_j(t)-Pmicro-j(t)-PH_l(t)
(14)；
At this time, the stored energy of the composite energy storage system
EH_l(t+1)＝ELi_l(t+1)+ESC_l(t+1)
＝ELi_l(t)ηLi+ESC_l(t)ηSC
+[PG(t)+Pmicro_l(t)-PL-j(t)]Δt
(15)；
At this time, the missing power output is the required real-time power, as shown in the following formula:
Figure BDA0002788143470000121
in the formula, Pq(t) represents the missing power output of the hybrid energy storage system; pG(t) represents the real-time power required to surf the internet.
After the 400V bus in the active power distribution network is added into the energy storage system, the objective function f takes the minimum power shortage rate of the power grid system as the optimization target2Comprises the following steps:
Figure BDA0002788143470000131
where T is an operation cycle × time period, which indicates a calculation time period, PL(t) represents the total load power on the 400V bus.
In this embodiment, T is 24 × 365.
The constraints need to satisfy the power balance of equations (2), (7), (9), (10), (11), (12).
And fourthly, performing multi-objective optimization on the configuration scheme according to the self-adaptive function and the self-adaptive weight, and realizing reasonable capacity configuration of the composite energy storage system.
When the capacity configuration optimization of the composite energy storage system added into the active power distribution network is researched, the selected optimization target is smooth distributed energy power output, and the minimum absolute value of the adjusted power change difference value is used as the optimization target. After the 400V bus in the active power distribution network is added with the energy storage device, the load defect rate is the minimum as the optimization target. And performing multi-objective optimization on the two, wherein an objective function f is as follows:
f＝min(c1f1+c2f2) (18)；
in the formula, c1、c2Represents a weight coefficient and satisfies c1+c2＝1。
The constraint conditions satisfied are formula (1), formula (2), formula (7), formula (9) -formula (12).
And solving the multiple targets by adopting an improved genetic algorithm.
Consider the minimization problem with 2 objectives:
min{z1＝f1(x),z2＝f1(x)} (19)；
for a given individual x, the objective function f is normalized:
Figure BDA0002788143470000141
in the formula, add
Figure BDA0002788143470000142
In order to normalize the objective function f to be within the range of 0-1 interval, after the objective function f is weighted, the formula (20) is converted into the solving range within the range of 0-2 interval.
The optimized objective function f using the adaptive penalty function and the adaptive weights is:
Figure BDA0002788143470000143
wherein r represents the number of objective functions and z represents the number of chromosomes; f. ofr,z,maxThe maximum value output by the objective function r when the optimization is carried out by adopting an improved genetic algorithm is shown; f. ofr,z,minRepresents the minimum value output by the objective function r when the optimization is carried out by adopting an improved genetic algorithm.
The expression of the adaptive penalty function is:
Figure BDA0002788143470000144
in the formula,. DELTA.Pz(x) The sum of unsatisfied power deviation values of the system node energy storage system calculated by the z-th individual x in the population is represented;
ΔEz(x) Indicating that the z-th individual x in the population does not meet the sum of capacity deviation values;
ΔSOCz(x) Calculating the sum of the deviation values which do not meet the system charge state for the z-th individual x in the population;
Figure BDA0002788143470000145
the most severe values for which the power, capacity and state of charge in the population do not meet the requirements are respectively.
In the application, the adaptive penalty function and the adaptive weight genetic algorithm are adopted to solve multiple targets, so that the reasonable capacity configuration of the composite energy storage system in the active power distribution network is realized, and the economic operation of the active power distribution network is realized.
Detailed description of the invention
An embodiment of the present invention provides a terminal device for capacity configuration of an active power distribution network based on composite energy storage, where the terminal device in the embodiment includes: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the computing steps of embodiment 1 when executing the computer program,
the processor, when executing the computer program, implements the functions of the modules/units in the above device embodiments, for example:
illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used for describing the execution process of the computer program in the active power distribution network capacity configuration terminal equipment based on composite energy storage. For example, the computer program may be divided into a plurality of modules, each module having the following specific functions:
1. the model building module is used for building a composite energy storage system model applied to the active power distribution network;
2. the distributed power supply analysis module is used for analyzing the configuration of the composite energy storage capacity during distributed power supply;
3. the micro-grid analysis module is used for analyzing the energy storage optimization configuration of each micro-grid group;
4. and the multi-objective optimization module is used for optimizing objective functions in the distributed power analysis module and the micro-grid analysis module.
The active power distribution network capacity configuration terminal equipment based on the composite energy storage can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The capacity configuration terminal device of the active power distribution network based on the composite energy storage can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the above examples are merely examples of the terminal device configured based on the capacity of the active power distribution network based on the composite energy storage, and do not constitute a limitation on the terminal device configured based on the capacity of the active power distribution network based on the composite energy storage, and may include more or less components than those shown in the drawings, or combine some components, or different components, for example, the terminal device configured based on the capacity of the active power distribution network based on the composite energy storage may further include an input and output device, a network access device, a bus, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the active power distribution network capacity configuration terminal equipment based on the composite energy storage, and various interfaces and lines are used for connecting various parts of the whole active power distribution network capacity configuration terminal equipment based on the composite energy storage.
The memory can be used for storing the computer programs and/or modules, and the processor realizes various functions of the active power distribution network capacity configuration terminal equipment based on composite energy storage by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Detailed description of the preferred embodiment
The module/unit integrated with the capacity configuration terminal device of the active power distribution network based on the composite energy storage can be stored in a computer readable storage medium if the module/unit is realized in the form of a software functional unit and is sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The embodiments of the present invention are preferred embodiments of the present invention, and the scope of the present invention is not limited by these embodiments, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.

Claims (10)

1. A capacity configuration method of an active power distribution network based on composite energy storage is characterized by comprising the following steps: the method comprises the following steps:
s1, establishing a composite energy storage system model applied to the active power distribution network;
s2, analyzing the composite energy storage capacity configuration in the distributed power supply based on the composite energy storage system model to obtain a first capacity configuration scheme;
s3, analyzing energy storage optimization configuration of each micro-grid group based on the composite energy storage system model to obtain a second capacity configuration scheme;
and S4, performing multi-objective optimization on the first and second capacity allocation schemes according to the adaptive function and the adaptive weight, and realizing reasonable capacity allocation of the composite energy storage system.
2. The capacity configuration method for the active power distribution network based on the composite energy storage is characterized by comprising the following steps of: in step S1, the composite energy storage system model includes at least one microgrid and a composite energy storage device, and the composite energy storage device includes a super storage battery and a super capacitor; the super storage battery is connected to the direct current bus through a first conversion power supply; the super capacitor is connected to the direct current bus through a second conversion power supply; the distributed energy is connected to the microgrid system through the converter; each micro-grid comprises a power type energy storage mode and an energy type energy storage mode, and each micro-grid and the composite energy storage device are connected in parallel.
3. The capacity configuration method for the active power distribution network based on the composite energy storage is characterized by comprising the following steps of: in step S2, the power of the composite energy storage system is analyzed, a target function and a constraint condition are established, and a second capacity allocation scheme is obtained.
4. The capacity configuration method for the active power distribution network based on the composite energy storage is characterized by comprising the following steps of: composite energy storage system power PH(t) is represented by the following formula:
PH(t)＝PLi(t)+PSC(t)＝PN(t)-Pout(t) (1)；
rated output power P of composite energy storage systemHESSThe following formula:
Figure FDA0002788143460000021
rated output energy E of composite energy storage systemHESSSatisfies the following formula:
Figure FDA0002788143460000022
in the formula, PLi(t) represents the output power of the lithium battery; pSC(t) represents the output power of the super capacitor; pN(t) represents the output power of the distributed power supply; pout(t) represents the output power supplied to the hybrid; eta1Represents the efficiency of the DC-DC conversion power supply; eta2Represents the efficiency of the DC-AC conversion power supply; etaLiRepresents the charging efficiency of the lithium battery; etaSCRepresenting the charging efficiency of the super capacitor; h represents the operation period duration; pN_min(t) represents a minimum value of distributed energy output at a certain time; pout_max(t) represents a maximum load demand at a time; pLi_0(t) represents the real-time power of the lithium battery; pSC_0(t) represents the real-time power of the supercapacitor; SOCLi_0Indicating initial state of charge of lithium battery；SOCLi_maxRepresents the upper limit of the state of charge of the lithium battery; SOCLi_minRepresents a lower limit of the state of charge of the lithium battery; SOCSC_0Representing the initial state of charge of the super capacitor; SOCSC_maxRepresents the upper limit of the state of charge of the super capacitor; SOCSC_minRepresenting the lower limit of the state of charge of the supercapacitor.
5. The capacity configuration method for the active power distribution network based on the composite energy storage is characterized by comprising the following steps of: establishing a first objective function f by taking the minimum absolute value of the adjusted power change difference value as an optimization target1Comprises the following steps:
Figure FDA0002788143460000023
the constraint conditions include: the storage battery works under the condition that the storage battery does not exceed a set interval; the output energy change meets the difference change between the distributed energy and the load; the requirement of maximum power fluctuation is met, and if the power supply of the distributed energy source is stopped suddenly, the energy storage device needs to respond and support quickly;
in the formula, PH_0(t) represents the real-time power of the composite stored energy output.
6. The capacity configuration method for the active power distribution network based on the composite energy storage is characterized by comprising the following steps of: in step S3, after the microgrid bus is connected to the energy storage system, a second objective function f is established with the minimum power shortage of the system as an optimization objective2And a constraint, wherein the second objective function f2Comprises the following steps:
Figure FDA0002788143460000031
in the formula, Pq(t) power output lacking in the system; pL(t) represents the total load power on the bus; t is a calculation time period.
7. According toThe capacity configuration method for the active power distribution network based on the composite energy storage as claimed in claim 6, wherein the capacity configuration method comprises the following steps: the constraint conditions include: meet the configuration rated output power P of the energy storage systemHESS(ii) a Energy storage system configuration rated output energy EHESS(ii) a The storage battery works under the condition of not crossing the wire; the output energy change meets the difference change between the distributed energy and the load; the requirement of maximum power fluctuation is met, and if the power supply of the distributed energy source is stopped suddenly, the energy storage device needs to respond and support quickly; under the condition of dynamic balance among each micro-grid group, the output power P of the composite energy storage systemH_1(t) satisfies:
Figure FDA0002788143460000032
in the formula, PH_1(t) representing the real-time output power of the microgrid; n is the number of nodes of the micro-network; pL_j(t) represents the load power on the bus; m represents the number of loads, PG(t) represents the net real time power.
8. The capacity configuration method for the active power distribution network based on the composite energy storage is characterized by comprising the following steps of: in step S4, a first objective function f in the first capacity allocation plan is configured1Second objective function f in a second capacity allocation scheme2Performing multi-objective optimization to obtain an objective function f as:
f＝min(c1f1+c2f2) (18)；
combining constraint conditions, solving the multiple targets by adopting an improved genetic algorithm, and normalizing to obtain an optimized target function f:
Figure FDA0002788143460000041
wherein r represents the number of objective functions and z represents the number of chromosomes; f. ofr,z,maxThe maximum value output by the objective function r when the optimization is carried out by adopting an improved genetic algorithm is shown; f. ofr,z,minRepresents the minimum value output by the objective function r when the optimization is carried out by adopting an improved genetic algorithm.
9. An active power distribution network capacity configuration terminal based on composite energy storage, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: the processor, when executing the computer program, implements the method of any of claims 1-8.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
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